Whole-genome sequencing of cryopreserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines.

BACKGROUND: Numerous genomic scans for positive selection have been performed in livestock species within the last decade, but often a detailed characterization of the detected regions (gene or trait under selection, timing of selection events) is lacking. Cryopreserved resources stored in reproductive or DNA gene banks offer a great opportunity to improve this characterization by providing direct access to recent allele frequency dynamics, thereby differentiating between signatures from recent breeding objectives and those related to more ancient selection constraints. Improved characterization can also be achieved by using next-generation sequencing data, which helps narrowing the size of the detected regions while reducing the number of associated candidate genes. METHODS: We estimated genetic diversity and detected signatures of recent selection in French Large White pigs by sequencing the genomes of 36 animals from three distinct cryopreserved samples: two recent samples from dam (LWD) and sire (LWS) lines, which had diverged from 1995 and were selected under partly different objectives, and an older sample from 1977 prior to the divergence. RESULTS: French LWD and LWS lines have lost approximately 5% of the SNPs that segregated in the 1977 ancestral population. Thirty-eight genomic regions under recent selection were detected in these lines and the corresponding selection events were further classified as convergent between lines (18 regions), divergent between lines (10 regions), specific to the dam line (6 regions) or specific to the sire line (4 regions). Several biological functions were found to be significantly enriched among the genes included in these regions: body size, body weight and growth regardless of the category, early life survival and calcium metabolism more specifically in the signatures in the dam line and lipid and glycogen metabolism more specifically in the signatures in the sire line. Recent selection on IGF2 was confirmed and several other regions were linked to a single candidate gene (ARHGAP10, BMPR1B, GNA14, KATNA1, LPIN1, PKP1, PTH, SEMA3E or ZC3HAV1, among others). CONCLUSIONS: These results illustrate that sequencing the genome of animals at several recent time points generates considerable insight into the traits, genes and variants under recent selection in a population. This approach could be applied to other livestock populations, e.g. by exploiting the rich biological resources stored in cryobanks.

Gene Banks as Reservoirs to Detect Recent Selection: The Example of the Asturiana de los Valles Bovine Breed.

Gene banks, framed within the efforts for conserving animal genetic resources to ensure the adaptability of livestock production systems to population growth, income, and climate change challenges, have emerged as invaluable resources for biodiversity and scientific research. Allele frequency trajectories over the few last generations contain rich information about the selection history of populations, which cannot be obtained from classical selection scan approaches based on present time data only. Here we apply a new statistical approach taking advantage of genomic time series and a state of the art statistic (nSL) based on present time data to disentangle both old and recent signatures of selection in the Asturiana de los Valles cattle breed. This local Spanish originally multipurpose breed native to Asturias has been selected for beef production over the last few generations. With the use of SNP chip and whole-genome sequencing (WGS) data, we detect candidate regions under selection reflecting the effort of breeders to produce economically valuable beef individuals, e.g., by improving carcass and meat traits with genes such as MSTN, FLRT2, CRABP2, ZNF215, RBPMS2, OAZ2, or ZNF609, while maintaining the ability to thrive under a semi-intensive production system, with the selection of immune (GIMAP7, GIMAP4, GIMAP8, and TICAM1) or olfactory receptor (OR2D2, OR2D3, OR10A4, and 0R6A2) genes. This kind of information will allow us to take advantage of the invaluable resources provided by gene bank collections from local less competitive breeds, enabling the livestock industry to exploit the different mechanisms fine-tuned by natural and human-driven selection on different populations to improve productivity.

Inference of Selection from Genetic Time Series Using Various Parametric Approximations to the Wright-Fisher Model.

Detecting genomic regions under selection is an important objective of population genetics. Typical analyses for this goal are based on exploiting genetic diversity patterns in present time data but rapid advances in DNA sequencing have increased the availability of time series genomic data. A common approach to analyze such data is to model the temporal evolution of an allele frequency as a Markov chain. Based on this principle, several methods have been proposed to infer selection intensity. One of their differences lies in how they model the transition probabilities of the Markov chain. Using the Wright-Fisher model is a natural choice but its computational cost is prohibitive for large population sizes so approximations to this model based on parametric distributions have been proposed. Here, we compared the performance of some of these approximations with respect to their power to detect selection and their estimation of the selection coefficient. We developped a new generic Hidden Markov Model likelihood calculator and applied it on genetic time series simulated under various evolutionary scenarios. The Beta with spikes approximation, which combines discrete fixation probabilities with a continuous Beta distribution, was found to perform consistently better than the others. This distribution provides an almost perfect fit to the Wright-Fisher model in terms of selection inference, for a computational cost that does not increase with population size. We further evaluated this model for population sizes not accessible to the Wright-Fisher model and illustrated its performance on a dataset of two divergently selected chicken populations.